ML Research Engineer

Company Description: Anthology is a community of exceptional engineers and scientists ("Fellows") focused on training the next generation of AI models. Fellows use domain expertise to identify where current models fail, and then create high-quality training environments and data to teach AI how to improve on realistic engineering and research workflows.
We work with leading labs to accelerate progress on the hardest problems in software engineering, biology, medicine, materials science, energy, and beyond. Anthology is part of Polymath, which is backed by Base10, Founders Future, Y Combinator, and other incredible investors.
Role Description: Anthology is looking for engineers and researchers to create and peer-review technical tasks for training and evaluating AI coding agents. As an Anthology Fellow, you’ll turn problems from your specialty into tasks with clear objectives, working reference solutions, and verifiers. You’ll also review tasks created by other Fellows, checking their technical correctness, difficulty, and quality.
You should have experience in one ore more of the following:
Kernels: CUDA or Triton, GPU performance, operator fusion, memory optimization, and numerical correctness
Inference: model serving, speculative decoding, quantization, caching, batching, and streaming
Training: pretraining, fine-tuning, distributed training, optimization, and training-pipeline debugging
Data engineering: data pipelines, corpus curation, entity resolution, deduplication, and data quality
Algorithms: graph algorithms, combinatorial optimization, and numerical methods
In this role, you will:
Propose original tasks grounded in realistic kernel and inference optimization problems.
Write specifications that define the target workload, hardware, constraints, success criteria, and intended failure modes.
Build reproducible environments, starting code, baseline implementations, and reference solutions.
Develop automated evaluators that check numerical correctness and measure performance, such as speedup, throughput, latency, and memory use.
Test tasks against AI agent trials and investigate unexpected results.
Peer-review other Fellows' proposals, implementations, and evaluators within your expertise.
Run reference solutions, check edge cases, and identify ambiguous requirements, unreliable measurements, or shortcuts that could pass evaluation without solving the intended problem.
Give specific, actionable feedback and revise your own tasks in response to review.
You should bring:
Substantial engineering, research, or open-source experience writing or optimizing GPU kernels or inference systems.
Experience with independently debugging complex systems or implementing technically demanding algorithms.
Strong testing skills, including checking numerical correctness across data types, shapes, and edge cases.
Clear technical writing and the ability to explain why a solution or evaluation is sound.
Comfort with reviewing code, questioning assumptions, and discussing technical trade-offs.
Experience building benchmarks, evaluation frameworks, or reinforcement learning environments is useful. Relevant domain expertise and sound engineering judgment are central to the role.
Compensation:
South & Southeast Asia / Latin America / Eastern Europe / Africa: $25–$80/hr
Compensation is based on experience, demonstrated technical depth, domain specialization, location, and the complexity and quality of work you are able to independently own.
How to apply: Submit an application on our website here!
https://anthologyresearch.ai/roles/ml-engineer-c26ed3
After that, the first step is a casual AI voice interview, around 20 minutes long. We’ll discuss your background and go through some technical questions related to your field. No preparation is required to succeed in the interview.

Work arrangement
Yes